Evaluating the Tamm–Dancoff Approximation for Two-Photon Absorption Properties
Bibliographic record
Abstract
Two-photon absorption (2PA) is a powerful tool in nonlinear optics, enabling advanced applications like high-resolution microscopy and photodynamic therapy. In this study, we explore the 2PA properties of 20 chromophores using time-dependent density-functional theory (TD-DFT), TD-DFT with the Tamm–Dancoff approximation (TDA), and the resolution-of-identity approximation in conjunction with the second-order approximate coupled-cluster (RI-CC2). TDA’s performance in predicting 2PA properties remains insufficiently characterized compared to (full) TD-DFT, where the functionals CAM-B3LYP, MN15, and ωB97X are used, and their results are compared to RI-CC2. This comparison involves assessing key photophysical properties, including 2PA cross sections (σ 2PA ) and dipole moments (μ 00, μ 11, μ 01, and Δμ). Among the tested functionals, MN15 demonstrated the lowest mean absolute errors (MAEs) for the computed photophysical properties for both TDA and TD-DFT. Our findings indicate that TDA captures qualitative trends and provides slightly more accurate quantitative predictions with generally lower MAEs than TD-DFT. These findings suggest that the use of TDA has significant potential as a cost-effective alternative to (full) TD-DFT for predicting 2PA properties.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".